1. Field of the Invention
The present invention relates to the delivery of material to tissue within a subject or patient. In particular, delivery of materials such as therapeutic, image enhancing, bio-active, pharmacological, nanotechnical or otherwise active materials is enhanced, particularly under real-time observable imaging systems (such as MRI, sonograms, and X-ray fluoroscopy). The invention further relates to the field of predictive mass transport or diffusion analysis for enhancing material delivery within a subject or patient.
2. Background of the Art
It is increasingly common to administer a drug or other material to a carefully targeted part of the body, rather than to insert it into the bloodstream and rely on some of it finding the target by carriage through the circulatory system. This targeted delivery has multiple advantages, when it can be performed effectively. Among the advantages of targeted delivery are that much less of the drug is needed, which itself represents a double gain. The drug itself is often costly, so reducing the volume of drug used in a treatment can represent a significant cost savings. It is also rare that any drug is wholly without negative effects (xe2x80x98side effectsxe2x80x99 with reference to the desired result). Where these adverse effects arise only where the drug reaches a specific non-targeted tissue, restricting the drug to a target that does not include that non-targeted tissue avoids the side effects completely. Even where this complete exclusion is impossible, the side effects may be far more acceptable if limited to a small region around the target, with reduced impact on the body at large, while delivering the desired result on the target tissue at full strength. Even where the side effects are not directly life-threatening, it can be important to avoid them: for example, to keep cancer chemotherapy drugs from the sites where they cause nausea and hair loss is good both for patient morale and for patient persistence in taking the drug. The importance of targeted delivery will increase for nanodevices (that is, devices with dimensions that are measured with one or two orders of magnitude of nanometers), which will often be designed for highly specific activity in a particular environment. Apart from the waste of resources in failure to reach the target site, their action in unintended (and less studied) sites may be hard to predict.
However, this format of targeted material delivery adds to the traditional complexity of computing dosage and delivery rates. Instead of a single figure of blood concentration, controlled by the rates at which the drug enters the circulatory system and at which it diffuses, flows and is absorbed, metabolized or excreted by various tissues, concentration becomes a distinct time-varying value (number) at each part of the body. Since the effects of a drug vary in complex ways depending upon local levels of concentration (scopolamine, for example, is an anti-nausea drug in a narrow range of levels), its administration must ensure that at active points in the target tissue the concentration is correct across the entire region of targeted tissue to obtain the desired effect, while at other non-active treatment points minimizing undesired effects (often, but not always, by minimizing concentration at such points). Planning delivery commensurate with desired treatment effects thus requires a difficult prediction task.
The need for such prediction of delivery and diffusion profiles has increased, particularly with the recent development of methods of tracking a diffusing or flowing material in real time in the patient (see U.S. Pat. No. 6,026,316) and with the advent of direct drug infusion techniques (see U.S. Pat. No. 5,720,720, which describes a catheter-based technique for high-flow microinfusion, and U.S. Pat. No. 5,735,814, which discusses drug infusion into brain tissue by means of an implantable pump and catheter). The physician can thus observe the changes in concentration in the various tissues around the entry point and delivery region, and modify plans according to observed events. It is important to note that the ability to modify or alter the significant results are not effectable instantaneously. Unlike an artist applying paint to a canvas, where the result is immediate, the physician must control the administration process according to events that will subsequently provide observable or therapeutic effects that result over a period of seconds or minutes. However, the decision itself must be immediate, so the prediction of consequences must be available immediately in advance of the alteration of procedures.
The physician""s brain, or a computer in assistance, must model the concentration dynamics prospectively much faster than they occur in real time, to be useful in real-time decision making. However, current computational methods of predicting concentration dynamics in tissue take longer than the actual events in the process of delivery. These current methods rely on two steps, both of which are slow.
The first step, once a scan of the target region is available (in either preoperative or real time during initial steps of the medical procedure), is to build a structural model of the tissue structures present. This requires first the labeling of points according to the type of tissue present (this process is often called xe2x80x98segmentationxe2x80x99, since it categorizes the points into three-dimensional xe2x80x98segmentsxe2x80x99). FIG. 1 illustrates this with a 2D slice of a brain scan, with the Globus Pallidus Medialis on each side extracted and marked visually (101) by a uniform gray shade. FIG. 2 shows the layered 3D region constructed from such slices. The planner then creates a best-fit geometric model of each structure present, such as bone, hippocampus, cortex, etc. FIG. 3 shows such a 3D model, for one of the segments identified in FIG. 1. Note that xe2x80x98modelxe2x80x99 in this sense is a description of a geometrical shape, by (for instance) specifying vertices and faces, rather than a statistical model of a relationship, found by such methods as least squares. The decision as to what model fits the data best is somewhat heuristic: usually the objectives in fitting a geometric surface model to a bone are that points inside it should mostly be xe2x80x98probably bonexe2x80x99 on the evidence of local scan values, that they should form a connected region, that points immediately outside should be xe2x80x98probably not bonexe2x80x99, and that the surface should be reasonably smooth. (This last criterion tends both to reduce the impact of noisy data, and to allow a model that uses fewer vertices faces.) Methods for constructing such a surface model range from local definition of a surface that separates points according to whether they are above or below a threshold value, such as the Marching Cubes technique [W E Lorensen and H E Cline, System and method for the display of surface structures contained within the interior region of a solid body, U.S. Pat. No. 4,710,876] to active xe2x80x98balloonsxe2x80x99 that move over the 3D image and attach themselves to boundary-like points, while resisting extremes of bending. (See for example L Cohen, L D Cohen, and N Ayache, xe2x80x9cUsing deformable surfaces to segment 3-D images and infer differential structures,xe2x80x9d CVGIP:19, Image Understanding 56(2):242-263, September 1992)
These models are divided into finite elements with simple geometric forms, such as tetrahedra (as in FIG. 3) or skewed cuboids, spheres or other geometric or mathematical shapes. On each such element, a partial differential equation governing concentration dynamics, which by the definition of xe2x80x98differentialxe2x80x99 involves values at an infinitude of points, is approximated. This approximation is by a system of equations with a small or at least controlled number of variables. Typically each variable multiplies a fixed function of position before it is added to an approximation of the concentration function. In the simplest cases of current art, these functions may be constants and linear functions, such as functions proportional to x, proportional to y and proportional to z axes in the volume. In more complex approximations, they may be polynomial functions, wavelets, etc. The interactions between the scalar coefficients of these functions, within a finite element and between neighbors, are chosen to reflect the local rates of diffusion, flow, absorption, metabolism, excretion, etc., in a xe2x80x98lumpedxe2x80x99 fashion over each finite element, so that the second step, computing the evolution of the approximated concentration function, gives results approximating those that would be predicted by a solution of the underlying partial differential equation.
The model-construction step is a relatively long procedure. It is not easily automated, and yet it must be complete before any predictions are possible. (FIG. 3 represents many hours of technical work.) Currently it takes much more time than would be acceptable in clinical practice, creating a delay between a preliminary scan and the administration of a drug. The scarce and costly expert labor required for performing this step is another obstruction to deployment. When the prediction is complete, the predictive calculations may still remain too slow for real-time guidance of the drug administration process.
The present invention exploits the new possibility of three-dimensional drug tracking, mentioned above, to create computational models of another kind, in a much more automatic way. Moreover, the equations can be solved rapidly enough for clinical use.
The methods described in U.S. Pat. No. 6,026,316; and instruments used in U.S. Pat. No. 5,964,705 and copending U.S. Patent Applications bearing Ser. Nos. 09/532,145, 09/532,667 and Ser. No. 09/532,037 filed on Mar. 21, 2000, make it possible to administer a trackable material at one or more chosen points in the organism, and record the resulting change over time of the material""s concentrations at a large number of points. From this information may be estimated the characteristic rates at which the tissue at these points permits the diffusion and other processes by which the concentration evolves. For example, if the concentrations at two neighboring points are always very similar, diffusion rates between those points must be larger than diffusion rates between nearby points where substantial differences in concentration persist. In quantifying these deductions or estimates, one must treat all the terms of the concentration dynamics simultaneously, since the action of each term of the concentration has had impact on the available data. These estimates may be refined by certain forms of a priori knowledge; for example, bone can be recognized by static aspects of the scan data, without reference to recorded concentrations, and diffusion and flow are known to be slow in bone. Using such scan data, with segmentation information where convenient, but without constructing a geometrical model specifically representing the segmented tissues, we construct a field of coefficients (such as direction-dependent diffusion rates, flow velocity of any fluid which transports the material, absorption rates, excretion rates, and any other processes important to the concentration dynamics). This creates a highly regular structure (e.g., FIG. 4, and FIG. 5) of data and evolution rules, varying from point to point only by the static local coefficients. We then model the evolution of the concentration either by a field of values (where the static local coefficients control the disappearance rate of the material and the interactions between neighboring concentration values), or by a population of moving particles whose probability of transfer between neighboring points, or of disappearance, is governed by static local coefficients. FIG. 6 illustrates this standard method-for the case of a 2D field evolving by diffusion only. A group of modeled particles starting at point 601 will spread out more rapidly into a dilute cloud than a group starting at point 602, if their motion is computed according to the constants in FIG. 5.
The creation of such data structures may be largely automated, following the flow indicated in FIG. 7. The process is computation-intensive, but can be greatly speeded by the use of parallel computing: an option is to use a highly pipelined machine such as a Cray mainframe computer, butxe2x80x94particularly with the rapid increase in machine speedsxe2x80x94a multiprocessor PC will suffice for an increasing portion of the functionality of the present invention. For a small fraction of the cost of the imaging device such as an MRI scanner that is providing the data, a chip or chipset optimized for this particular task will enhance the power of the images in guiding intervention. The automated process reduces delay and costs to levels acceptable for clinical deployment, and the concentration computations are of a type highly susceptible to computational speedup by parallelization methods, such as the use of pipelined vector architecture for a field of concentration values, or of xe2x80x98single instruction multiple dataxe2x80x99 (SIMD) architecture to track a population of particles. This format enables computation to predict the results of particular administration strategies fast enough so that plans can be compared. An optimal plan can be chosen, in a clinically acceptable time. The plan can be implemented while the material administration procedure is in progress to track deviations from the expected evolution of concentrations, to deduce corrections in the static coefficients, and to offer both revised predictions of the results of the current strategy and predictions of possible alternative strategies. All of this may be done in time to be useful to the controller.